Measurement, spatial spillover and influencing factors of agricultural carbon emissions efficiency in China

The efficiency of agricultural carbon emissions is a bridge between crop production and emission reduction, acting as a critical indicator of the potential for emission mitigation in agricultural production. In previous estimations, the outcomes yield the input-output efficiency of agriculture under the carbon emission constraint, rather than the efficiency of agricultural carbon emission, due to failing to separate the contribution of carbon emissions from other factors. To optimize the existing idea and understand the efficiency more precisely, a theoretical framework and a corresponding equation were developed for analysis in this study. In agricultural production, given the input factors, the efficiency of agricultural carbon emissions under the prerequisite of no desirable output was defined as the ratio of the minimum possible emissions to the actual emissons. On this basis, the GB-US-SBM model was employed to calculate the slack of emissions in 30 Chinese provinces from 2000 to 2019, reflecting the distance between the actual emission and production frontier. Then, the efficiency was estimated based on the slacks and actual emissions. Finally, the influencing factors and spillover effects of agriculural carbon emissions efficiency were explored using the spatial Durbin model. Results showed that: (1) From 2000 to 2019, the average agricultural carbon emissions efficiency was 0.778 in China, indicating considerable potential for emission reduction. At the provincial level, only Inner Mongolia and Qinghai had an efficiency of 1.000, while the rest of the provinces had different spaces for emission mitigation. (2) According to the emissions quantity and efficiency, the 30 provinces were divided into four groups. The five provinces, Henan, Hebei, Shandong, Heilongjiang, and Guangxi, belonged to a group of high emissions with high efficiency. The group of low emissions with high efficiency accounted for the majority, including 12 provinces, such as Inner Mongolia and Gansu. The group with high emissions and low efficiency covered seven provinces, such as Hunan and Hubei. Six provinces, including Zhejiang and Fujian, were classified as low emissions with low efficiency. (3) The global Moran’s index was significantly greater than 0, with a P-value under 0.01, verifying that there was a positive spatial autocorrelation in the provinces. The spatial econometric regression showed that efficiency had a significant positive spatial spillover effect, suggesting that an interactive evolution existed among close provinces. Specifically, four factors—industry structure, investment intensity, financial support for agriculture, and the degree of disaster, harmed the agricultural carbon emissions efficiency directly. By contrast, the irrigation effectiveness and urbanization indicated significant positive effects. In terms of spillover effects, the intensity of a disaster in a province negatively affected the efficiency of agricultural carbon emissions in neighboring provinces, while the urbanization rate exhibited a positive effect. Hence, it was essential to pay attention to the key factors that influence efficiency. Making full use of spillover effects could also help in achieving regional agricultural low-carbon transition. Additionally, local solutions should be addressed, owing to the regional characteristics of efficiency. This study results could provide a theoretical basis for the development of low-carbon agriculture in China.